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The dual role of a highly structured RNA (the S fragment) in the replication of foot-and-mouth disease virus.

Joseph C WardLidia Lasecka-DykesSamuel J DobsonSarah GoldNatalie J KingstonMorgan R HerodDonald P KingTobias J TuthillDavid J RowlandsNicola J Stonehouse
Published in: FASEB journal : official publication of the Federation of American Societies for Experimental Biology (2024)
Secondary and tertiary RNA structures play key roles in genome replication of single-stranded positive sense RNA viruses. Complex, functional structures are particularly abundant in the untranslated regions of picornaviruses, where they are involved in initiation of translation, priming of new strand synthesis and genome circularization. The 5' UTR of foot-and-mouth disease virus (FMDV) is predicted to include a c. 360 nucleotide-long stem-loop, termed the short (S) fragment. This structure is highly conserved and essential for viral replication, but the precise function(s) are unclear. Here, we used selective 2' hydroxyl acetylation analyzed by primer extension (SHAPE) to experimentally determine aspects of the structure, alongside comparative genomic analyses to confirm structure conservation from a wide range of field isolates. To examine its role in virus replication in cell culture, we introduced a series of deletions to the distal and proximal regions of the stem-loop. These truncations affected genome replication in a size-dependent and, in some cases, host cell-dependent manner. Furthermore, during the passage of viruses incorporating the largest tolerated deletion from the proximal region of the S fragment stem-loop, an additional mutation was selected in the viral RNA-dependent RNA polymerase, 3D pol . These data suggest that the S fragment and 3D pol interact in the formation of the FMDV replication complex.
Keyphrases
  • transcription factor
  • disease virus
  • nucleic acid
  • sars cov
  • high resolution
  • genome wide
  • single cell
  • machine learning
  • bone marrow
  • minimally invasive
  • mass spectrometry
  • genetic diversity
  • gene expression
  • big data